{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb6aa99c",
   "metadata": {},
   "source": [
    "# 基于昇思MindSpore+Orangepi AIpro的ShuffleNetV1图像分类\n",
    "\n",
    "功能：使用基于昇思MindSpore框架开发的Shufflenet模型对输入图片进行分类。  \n",
    "样例输入：原始Cifar20数据集。  \n",
    "样例输出：图片分类的结果。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1451fc2e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 前期准备\n",
    "\n",
    "* 基础镜像的样例目录中已包含转换后的om模型以及测试图片，如果直接运行，可跳过此步骤。如果需要重新转换模型，可以参考下面的步骤。\n",
    "* **建议在Linux服务器或者虚拟机转换该模型。**\n",
    "* 为了能进一步优化模型推理性能，我们需要将其转换为om模型进行使用；**转换指导详见全流程实验指导。**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82653e5f",
   "metadata": {},
   "source": [
    "# 模型推理实现\n",
    "\n",
    "* 注意：本案例在离线推理的过程中可能会出现内存不足的问题，可以根据情况查看FAQ文档中的解决方案。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95a2799-c2f5-405f-a4b1-722248655aa8",
   "metadata": {},
   "source": [
    "### 1. 导入三方库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a9e0abe-f4f5-4a29-8af8-4e082955d206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import acl\n",
    "\n",
    "import acllite_utils as utils\n",
    "import constants as const\n",
    "from acllite_model import AclLiteModel\n",
    "from acllite_resource import resource_list\n",
    "\n",
    "import mindspore as ms\n",
    "import mindspore.dataset as ds\n",
    "from mindspore.dataset import vision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30643cc9-9015-433b-8c31-66ed14cabdfe",
   "metadata": {},
   "source": [
    "### 2. 定义acllite资源初始化与去初始化类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b096cb19-e3e6-49fd-a38e-f55750f41154",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AclLiteResource:\n",
    "    \"\"\"\n",
    "    AclLiteResource\n",
    "    \"\"\"\n",
    "    def __init__(self, device_id=0):\n",
    "        self.device_id = device_id\n",
    "        self.context = None\n",
    "        self.stream = None\n",
    "        self.run_mode = None\n",
    "        \n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        init resource\n",
    "        \"\"\"\n",
    "        print(\"init resource stage:\")\n",
    "        ret = acl.init()\n",
    "\n",
    "        ret = acl.rt.set_device(self.device_id)\n",
    "        utils.check_ret(\"acl.rt.set_device\", ret)\n",
    "\n",
    "        self.context, ret = acl.rt.create_context(self.device_id)\n",
    "        utils.check_ret(\"acl.rt.create_context\", ret)\n",
    "\n",
    "        self.stream, ret = acl.rt.create_stream()\n",
    "        utils.check_ret(\"acl.rt.create_stream\", ret)\n",
    "\n",
    "        self.run_mode, ret = acl.rt.get_run_mode()\n",
    "        utils.check_ret(\"acl.rt.get_run_mode\", ret)\n",
    "\n",
    "        print(\"Init resource success\")\n",
    "\n",
    "    def __del__(self):\n",
    "        print(\"acl resource release all resource\")\n",
    "        resource_list.destroy()\n",
    "        if self.stream:\n",
    "            print(\"acl resource release stream\")\n",
    "            acl.rt.destroy_stream(self.stream)\n",
    "\n",
    "        if self.context:\n",
    "            print(\"acl resource release context\")\n",
    "            acl.rt.destroy_context(self.context)\n",
    "\n",
    "        print(\"Reset acl device \", self.device_id)\n",
    "        acl.rt.reset_device(self.device_id)\n",
    "        print(\"Release acl resource success\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ab3af4-e881-432e-a3f2-9461666f6dbc",
   "metadata": {},
   "source": [
    "### 3. 定义分类类，包含前处理、推理、结果展示等操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60b1bed4-24e4-4ece-8c53-41cf24a74eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classification(object):\n",
    "    \"\"\"\n",
    "    class for Classification\n",
    "    \"\"\"\n",
    "    def __init__(self, model_path):\n",
    "        self._model_path = model_path\n",
    "        self.device_id = 0\n",
    "        self._model = None\n",
    "\n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        Initialize\n",
    "        \"\"\"\n",
    "        # Load model\n",
    "        self._model = AclLiteModel(self._model_path)\n",
    "\n",
    "        return const.SUCCESS\n",
    "\n",
    "    def pre_process(self, input_path):\n",
    "        # 读取原始Cifar10数据集\n",
    "        dataset_predict = ds.Cifar10Dataset(dataset_dir=input_path, shuffle=False, num_samples=16)\n",
    "        # 进行一系列的数据前处理操作\n",
    "        image_trans = [\n",
    "            vision.RandomCrop((32, 32), (4, 4, 4, 4)),\n",
    "            vision.RandomHorizontalFlip(prob=0.5),\n",
    "            vision.Resize((224, 224)),\n",
    "            vision.Rescale(1.0 / 255.0, 0.0),\n",
    "            vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n",
    "            vision.HWC2CHW()\n",
    "                ]\n",
    "        dataset_predict = dataset_predict.map(image_trans, 'image')\n",
    "        # 设置batch_size\n",
    "        dataset_predict = dataset_predict.batch(1)\n",
    "        data_loader = dataset_predict.create_dict_iterator()\n",
    "\n",
    "        return data_loader\n",
    "    \n",
    "\n",
    "    def inference(self, data_loader):\n",
    "        \"\"\"\n",
    "        model inference\n",
    "        \"\"\"  \n",
    "        results = []\n",
    "        for i, resized_image in enumerate(data_loader):\n",
    "            # 读物数据集图片\n",
    "            result1 = ms.Tensor(resized_image['image'])\n",
    "            result = self._model.execute([result1.asnumpy(), ])\n",
    "            \n",
    "            # 选取概率最高的那个结果\n",
    "            pred = np.argmax(result[0], axis=1)\n",
    "            results.append(pred[0])\n",
    "        \n",
    "        return results, data_loader\n",
    "\n",
    "    def show_process(self, infer_output, data_origin, input_path):\n",
    "        \"\"\"\n",
    "        show process\n",
    "        \"\"\"\n",
    "        dataset_predict = ds.Cifar10Dataset(dataset_dir=input_path, shuffle=False, num_samples=16)\n",
    "        class_dict = {0:\"airplane\", 1:\"automobile\", 2:\"bird\", 3:\"cat\", 4:\"deer\", 5:\"dog\", 6:\"frog\", 7:\"horse\", 8:\"ship\", 9:\"truck\"}\n",
    "\n",
    "        # 推理效果展示(上方为预测的结果，下方为推理效果图片)\n",
    "        plt.figure(figsize=(16, 5))\n",
    "\n",
    "        for i, resized_image in enumerate(dataset_predict.create_dict_iterator()):\n",
    "            plt.subplot(2, 8, i+1)\n",
    "            plt.title('{}'.format(class_dict[infer_output[0][i]]))\n",
    "            plt.imshow(resized_image[\"image\"].asnumpy())\n",
    "            plt.axis(\"off\")\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafe334c-93d9-4469-b7e3-55e588018f82",
   "metadata": {},
   "source": [
    "### 4. 构造主函数，串联整个代码逻辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8143078",
   "metadata": {},
   "outputs": [],
   "source": [
    "from download import download\n",
    "\n",
    "# 获取数据集\n",
    "dataset_url = \"https://modelers.cn/coderepo/web/v1/file/MindSpore-Lab/cluoud_obs/main/media/examples/mindspore-courses/orange-pi-mindspore/03-ResNet50/cifar-10-batches-bin.zip\"\n",
    "download(dataset_url, \"./dataset\", kind=\"zip\", replace=True)\n",
    "\n",
    "# 获取模型om文件\n",
    "model_url = \"https://modelers.cn/coderepo/web/v1/file/MindSpore-Lab/cluoud_obs/main/media/examples/mindspore-courses/orange-pi-mindspore/06-ShuffleNet/shufflenet.zip\"\n",
    "download(model_url, \"./\", kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e3a384d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    # 模型\n",
    "    MODEL_PATH = \"shufflenet.om\"\n",
    "    # 数据集\n",
    "    input_path = \"./dataset/cifar-10-batches-bin\"\n",
    "\n",
    "    acl_resource = AclLiteResource()  # 初始化acl资源\n",
    "    acl_resource.init()\n",
    "    \n",
    "    # Instantiation Classification object\n",
    "    classification = Classification(MODEL_PATH)  # 构造模型对象\n",
    "    \n",
    "    # init\n",
    "    ret = classification.init()  # 初始化模型类变量\n",
    "    utils.check_ret(\"classification.init \", ret)  \n",
    "    \n",
    "    # preprocess\n",
    "    crop_and_paste_image = classification.pre_process(input_path)  # 前处理\n",
    "    \n",
    "    # inference\n",
    "    result = classification.inference(crop_and_paste_image)  # 推理\n",
    "\n",
    "    # show\n",
    "    classification.show_process(result, crop_and_paste_image, input_path)  # 结果展示\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd648da3-8d25-47d8-ace2-24fdd674dfa5",
   "metadata": {},
   "source": [
    "### 5. 运行\n",
    "运行完成后，会显示推理后的图片，如下所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "396dab1a-ced7-4b68-841c-810d0b3b65fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init resource stage:\n",
      "Init resource success\n",
      "Init model resource start...\n",
      "[AclLiteModel] create model output dataset:\n",
      "malloc output 0, size 40\n",
      "Create model output dataset success\n",
      "Init model resource success\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acl resource release all resource\n",
      "AclLiteModel release source success\n",
      "acl resource release stream\n",
      "acl resource release context\n",
      "Reset acl device  0\n",
      "Release acl resource success\n"
     ]
    }
   ],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a29d51d-0e2b-4875-808a-a2deb5c91d8d",
   "metadata": {},
   "source": [
    "# 样例总结"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2b4e3a-dd3d-4db0-bc76-55f4502ea8d9",
   "metadata": {},
   "source": [
    "我们来回顾一下以上代码，可以包括以下几个步骤：\n",
    "\n",
    "1. 初始化acl资源：在调用acl相关资源时，必须先初始化AscendCL，否则可能会导致后续系统内部资源初始化出错。\n",
    "2. 对图片进行前处理：在此样例中，我们首先根据图片路径，使用mindspore.dataset.Cifar10Dataset接口下加载CIFAR-10数据集，再利用mindspore.dataset.vision接口对数据集进行剪切、通道转换等操作，使得模型正常推理。然后基于数据集对象创建数据迭代器。\n",
    "3. 推理：利用AclLiteModel.execute接口对数据进行推理。\n",
    "4. 可视化推理结果：利用plt将结果画出。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  },
  "vscode": {
   "interpreter": {
    "hash": "5d4380103ee16a53c08c2072b2cf7372aea12e3655e7cb36be2874465cdcb878"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
